You are given three Pandas DataFrames for a factory: (1) events[event_id, machine_id, ts_utc (datetime64[ns, UTC]), event_type in {'start','stop','fault'}, batch_id], (2) telemetry[machine_id, ts_local (datetime64[ns]), temperature_C, rpm, power_kW, timezone (IANA string like 'US/Pacific')], (3) calendar[date (YYYY-MM-DD), is_holiday (bool), shift in {'A','B','C'}]. Data issues: late-arriving events up to 48 hours late, duplicate events (same event_id with ts_utc differences up to ±2 seconds), daylight saving transitions, and missing telemetry rows. Memory budget is 1 GB, total rows ≈50M. Tasks: a) Normalize all time to a single axis; deduplicate events with a deterministic rule (state your rule) while preserving correct event order. b) For the last 7 calendar days up to and including today=2025-09-01 in each machine’s local time, compute per-machine hourly features: throughput (count of completed start→stop cycles), 95th percentile temperature, and a rolling 24-hour z-score of power_kW. Handle missing hours and DST gaps/overlaps correctly. c) Join features into a tidy machine-hour panel indexed by [machine_id, hour_start_utc); impute missing values robustly; flag anomalies where |z|>3. Provide Pandas code snippets and explain performance tactics (chunked IO, dtypes, categoricals, Parquet, vectorized ops) and how you would test correctness on edge cases.